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266 lines
8.4 KiB
266 lines
8.4 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Modified from espnet(https://github.com/espnet/espnet)
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import librosa
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import numpy as np
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import pyworld
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from scipy.interpolate import interp1d
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class LogMelFBank():
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def __init__(self,
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sr: int=24000,
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n_fft: int=2048,
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hop_length: int=300,
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win_length: int=None,
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window: str="hann",
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n_mels: int=80,
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fmin: int=80,
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fmax: int=7600):
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self.sr = sr
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# stft
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self.n_fft = n_fft
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self.win_length = win_length
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self.hop_length = hop_length
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self.window = window
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self.center = True
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self.pad_mode = "reflect"
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# mel
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self.n_mels = n_mels
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self.fmin = 0 if fmin is None else fmin
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self.fmax = sr / 2 if fmax is None else fmax
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self.mel_filter = self._create_mel_filter()
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def _create_mel_filter(self):
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mel_filter = librosa.filters.mel(
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sr=self.sr,
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n_fft=self.n_fft,
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n_mels=self.n_mels,
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fmin=self.fmin,
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fmax=self.fmax)
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return mel_filter
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def _stft(self, wav: np.ndarray):
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D = librosa.core.stft(
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wav,
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n_fft=self.n_fft,
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hop_length=self.hop_length,
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win_length=self.win_length,
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window=self.window,
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center=self.center,
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pad_mode=self.pad_mode)
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return D
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def _spectrogram(self, wav: np.ndarray):
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D = self._stft(wav)
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return np.abs(D)
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def _mel_spectrogram(self, wav: np.ndarray):
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S = self._spectrogram(wav)
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mel = np.dot(self.mel_filter, S)
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return mel
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# We use different definition for log-spec between TTS and ASR
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# TTS: log_10(abs(stft))
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# ASR: log_e(power(stft))
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def get_log_mel_fbank(self, wav, base='10'):
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mel = self._mel_spectrogram(wav)
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mel = np.clip(mel, a_min=1e-10, a_max=float("inf"))
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if base == '10':
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mel = np.log10(mel.T)
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elif base == 'e':
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mel = np.log(mel.T)
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# (num_frames, n_mels)
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return mel
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class Pitch():
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def __init__(self,
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sr: int=24000,
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hop_length: int=300,
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f0min: int=80,
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f0max: int=7600):
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self.sr = sr
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self.hop_length = hop_length
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self.f0min = f0min
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self.f0max = f0max
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def _convert_to_continuous_f0(self, f0: np.ndarray) -> np.ndarray:
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if (f0 == 0).all():
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print("All frames seems to be unvoiced.")
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return f0
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# padding start and end of f0 sequence
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start_f0 = f0[f0 != 0][0]
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end_f0 = f0[f0 != 0][-1]
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start_idx = np.where(f0 == start_f0)[0][0]
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end_idx = np.where(f0 == end_f0)[0][-1]
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f0[:start_idx] = start_f0
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f0[end_idx:] = end_f0
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# get non-zero frame index
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nonzero_idxs = np.where(f0 != 0)[0]
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# perform linear interpolation
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interp_fn = interp1d(nonzero_idxs, f0[nonzero_idxs])
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f0 = interp_fn(np.arange(0, f0.shape[0]))
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return f0
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def _calculate_f0(self,
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input: np.ndarray,
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use_continuous_f0: bool=True,
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use_log_f0: bool=True) -> np.ndarray:
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input = input.astype(np.float)
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frame_period = 1000 * self.hop_length / self.sr
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f0, timeaxis = pyworld.dio(
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input,
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fs=self.sr,
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f0_floor=self.f0min,
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f0_ceil=self.f0max,
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frame_period=frame_period)
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f0 = pyworld.stonemask(input, f0, timeaxis, self.sr)
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if use_continuous_f0:
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f0 = self._convert_to_continuous_f0(f0)
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if use_log_f0:
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nonzero_idxs = np.where(f0 != 0)[0]
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f0[nonzero_idxs] = np.log(f0[nonzero_idxs])
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return f0.reshape(-1)
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def _average_by_duration(self, input: np.ndarray,
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d: np.ndarray) -> np.ndarray:
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d_cumsum = np.pad(d.cumsum(0), (1, 0), 'constant')
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arr_list = []
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for start, end in zip(d_cumsum[:-1], d_cumsum[1:]):
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arr = input[start:end]
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mask = arr == 0
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arr[mask] = 0
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avg_arr = np.mean(arr, axis=0) if len(arr) != 0 else np.array(0)
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arr_list.append(avg_arr)
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# shape (T,1)
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arr_list = np.expand_dims(np.array(arr_list), 0).T
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return arr_list
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def get_pitch(self,
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wav: np.ndarray,
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use_continuous_f0: bool=True,
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use_log_f0: bool=True,
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use_token_averaged_f0: bool=True,
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duration: np.ndarray=None):
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f0 = self._calculate_f0(wav, use_continuous_f0, use_log_f0)
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if use_token_averaged_f0 and duration is not None:
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f0 = self._average_by_duration(f0, duration)
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return f0
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class Energy():
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def __init__(self,
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n_fft: int=2048,
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hop_length: int=300,
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win_length: int=None,
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window: str="hann",
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center: bool=True,
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pad_mode: str="reflect"):
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self.n_fft = n_fft
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self.win_length = win_length
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self.hop_length = hop_length
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self.window = window
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self.center = center
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self.pad_mode = pad_mode
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def _stft(self, wav: np.ndarray):
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D = librosa.core.stft(
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wav,
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n_fft=self.n_fft,
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hop_length=self.hop_length,
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win_length=self.win_length,
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window=self.window,
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center=self.center,
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pad_mode=self.pad_mode)
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return D
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def _calculate_energy(self, input: np.ndarray):
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input = input.astype(np.float32)
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input_stft = self._stft(input)
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input_power = np.abs(input_stft)**2
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energy = np.sqrt(
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np.clip(
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np.sum(input_power, axis=0), a_min=1.0e-10, a_max=float('inf')))
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return energy
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def _average_by_duration(self, input: np.ndarray,
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d: np.ndarray) -> np.ndarray:
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d_cumsum = np.pad(d.cumsum(0), (1, 0), 'constant')
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arr_list = []
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for start, end in zip(d_cumsum[:-1], d_cumsum[1:]):
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arr = input[start:end]
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avg_arr = np.mean(arr, axis=0) if len(arr) != 0 else np.array(0)
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arr_list.append(avg_arr)
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# shape (T,1)
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arr_list = np.expand_dims(np.array(arr_list), 0).T
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return arr_list
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def get_energy(self,
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wav: np.ndarray,
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use_token_averaged_energy: bool=True,
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duration: np.ndarray=None):
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energy = self._calculate_energy(wav)
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if use_token_averaged_energy and duration is not None:
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energy = self._average_by_duration(energy, duration)
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return energy
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class LinearSpectrogram():
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def __init__(
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self,
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n_fft: int=1024,
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win_length: int=None,
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hop_length: int=256,
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window: str="hann",
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center: bool=True, ):
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self.n_fft = n_fft
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self.hop_length = hop_length
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self.win_length = win_length
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self.window = window
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self.center = center
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self.n_fft = n_fft
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self.pad_mode = "reflect"
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def _stft(self, wav: np.ndarray):
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D = librosa.core.stft(
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wav,
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n_fft=self.n_fft,
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hop_length=self.hop_length,
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win_length=self.win_length,
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window=self.window,
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center=self.center,
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pad_mode=self.pad_mode)
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return D
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def _spectrogram(self, wav: np.ndarray):
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D = self._stft(wav)
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return np.abs(D)
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def get_linear_spectrogram(self, wav: np.ndarray):
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linear_spectrogram = self._spectrogram(wav)
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linear_spectrogram = np.clip(
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linear_spectrogram, a_min=1e-10, a_max=float("inf"))
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return linear_spectrogram.T
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